This research addresses the helicopter controller synthesis and tuning
problem. A model-free ``teaching by showing'' approach is used to train
a fuzzy-neural controller for autonomous robot helicopters, in simulation
and hardware. A controller is generated and tuned using training data
gathered while a teacher operates the helicopter. This approach is useful
for time-varying systems for which mathematical models are unknown but
which can be stabilized and controlled by a human operator. The
methodology uses techniques from the fields of behavior-based control,
fuzzy logic, neural networks and teaching by showing, all of which are
model-free. A controller is decomposed by a human expert into a
hierarchical behavior-based control architecture with each behavior
implemented as a hybrid fuzzy logic controller (FLC) and general
regression neural network controller (GRNNC). The FLCs and GRNNCs are
generated through teaching by showing and they share in the control task.
The FLCs are built during initial controller generation, remain static
once created and provide coarse control of the helicopter. The GRNNCs are
incrementally built and modified whenever the controller does not meet
performance criteria, are dynamic and provide fine control, enhancing the
control of the FLCs. The methodology is applied both in simulation and on
a radio controlled (RC) model helicopter for real world validation. In
simulation, roll and pitch controllers were generated and tuned. They
were shown to be capable of meeting performance criteria for both
noise and noise-free test cases. However, when tested on actual hardware
the approach was inadequate. A roll controller generated using teaching
by showing could not meet desired performance criteria. This failure
demonstrates that simulation is not always enough to validate an approach
and why testing in the real world is both desirable and necessary. In
addition, we have been involved in the design, implementation and testing
of three RC model robotic helicopters. Case studies for these robots are
given.